A method for evaluating the conversion coefficient of hydroxyl radicals ∙OH produced by soil mineral oxidation

Abstract

The present invention relates to the technical field of environmental and agricultural data processing technology. The present invention provides a method for evaluating the conversion coefficient of hydroxyl radicals ∙OH produced by oxidation of soil minerals, which aims to solve the problem that it is difficult to fully simulate the complexity of the actual soil environment under laboratory conditions. The present invention includes pre-treating samples and experimental historical data, constructing a laboratory simulation model, and verifying its accuracy. By simulating the oxidation process of soil minerals under different environmental conditions, a kinetic model is constructed to record the amount of hydroxyl radicals produced and the law of change. The actual soil environmental data is collected and substituted into the model, the simulation and measured results are compared, and the applicability of the kinetic model is evaluated. Finally, the pre-treated field monitoring data is substituted into the evaluation model to calculate the conversion coefficient. The present invention improves the evaluation accuracy, reduces the experimental cost, enhances the applicability of the model, and provides a scientific basis for agricultural production and soil management.

Description

Technical Field

[0001] The present invention relates to the technical field of environmental and agricultural data processing, and provides a method for evaluating the conversion coefficient of hydroxyl radicals ∙OH produced by oxidation of soil minerals. Background Art

[0002] The evaluation of the conversion coefficient of soil mineral oxidation to produce hydroxyl radicals involves many aspects such as mineral type, environmental conditions, and redox reaction mechanism. Since the conversion coefficient is usually closely related to specific experimental conditions, mineral properties, and the kinetics of the oxidation reaction, it is difficult to give a universally applicable fixed value. In the soil environment, the process of mineral oxidation to produce hydroxyl radicals is often closely related to the reaction between ferrous ions and oxygen molecules. This process occurs through abiotic pathways, such as ferrous ions activating oxygen molecules to produce superoxide anions, which in turn generate hydrogen peroxide, and finally generate ∙OH through Fenton or Fenton-like reactions.

[0003] Under laboratory conditions, the oxidation process of soil minerals under different environmental conditions is simulated to determine the amount of hydroxyl radicals produced and their changes over time. This usually requires the use of specific chemical probes (such as benzoate) to capture and quantify hydroxyl radicals. Based on the results of laboratory simulation experiments and existing theoretical knowledge, a kinetic model for the production of hydroxyl radicals by soil mineral oxidation is constructed. The model can describe the process of hydroxyl radical production by mineral oxidation under different conditions and its main influencing factors. Field monitoring is carried out in actual soil environments to verify the effectiveness of laboratory simulation experiments and kinetic models. By comparing field monitoring data with model prediction results, the model parameters are further adjusted and improved.

[0004] There are many kinds of soil minerals, and the ability of each mineral to produce hydroxyl radicals by oxidation is different and is significantly affected by environmental conditions (such as temperature, humidity, pH value, organic matter content, etc.). The redox reaction mechanism is complex, involving multiple intermediates and reaction pathways, which are difficult to accurately describe and predict. It is difficult to fully simulate the complexity and dynamics of the actual soil environment under laboratory conditions, and it is impossible to accurately evaluate the conversion coefficient of hydroxyl radicals produced by soil mineral oxidation. Summary of the invention

[0005] In view of the shortcomings of the existing technology, the present invention provides a method for evaluating the conversion coefficient of hydroxyl radicals produced by soil mineral oxidation, which solves the problem that it is difficult to fully simulate the complexity and dynamics of the actual soil environment under laboratory conditions and it is impossible to accurately evaluate the conversion coefficient of hydroxyl radicals produced by soil mineral oxidation.

[0006] In order to solve the above technical problems, the specific technical solutions of the present invention are as follows:

[0007] The present invention provides a method for evaluating the conversion coefficient of hydroxyl radicals ∙OH produced by oxidation of soil minerals, comprising:

[0008] Step S101, acquiring sample data and experimental history data, preprocessing the sample data and the experimental history data to obtain preprocessed sample data and preprocessed experimental history data;

[0009] Step S102, using the preprocessed experimental historical data to build a laboratory simulation model, retrieve sample data from the database, substitute the sample data from the database into the preset laboratory simulation model, obtain experimental simulation results, compare the experimental simulation results with the historical experimental data in the database, if the data comparison results are consistent, the laboratory simulation model is qualified;

[0010] Step S103, receiving the sample data expected to be used, substituting the sample data expected to be used into the laboratory simulation model, generating simulation experiment results of the samples expected to be used, matching the simulation experiment results of the samples expected to be used with the sample data expected to be used, obtaining a simulation data set, and constructing a kinetic model of hydroxyl radical production by oxidation of soil minerals using the experimental history data and the simulation data set. The kinetic model of hydroxyl radical production by oxidation of soil minerals is used to simulate the oxidation process of soil minerals under different environmental conditions under laboratory conditions. The control parameters under different environmental conditions include controlling temperature, humidity, pH value, and added chemical reagents. The amount of hydroxyl radicals produced and the change pattern of hydroxyl radicals over time are recorded according to the oxidation process of soil minerals under different environmental conditions.

[0011] Step S104, collecting on-site monitoring data in the actual soil environment, the on-site monitoring data in the actual soil environment includes environmental parameters and data on hydroxyl radical production by soil mineral oxidation, substituting the on-site monitoring data in the actual soil environment into a kinetic model of hydroxyl radical production by soil mineral oxidation, outputting simulation results of hydroxyl radical production by soil mineral oxidation, comparing the simulation results of hydroxyl radical production by soil mineral oxidation with the on-site monitoring data in the actual soil environment, and if the error of the comparison result is within a preset range, evaluating the on-site monitoring data in the actual soil environment;

[0012] Step S105, preprocessing the field monitoring data in the actual soil environment, substituting the preprocessed field monitoring data in the actual soil environment into a preset evaluation model to obtain an evaluation result of the conversion coefficient of soil mineral oxidation to produce hydroxyl radicals.

[0013] Furthermore, the present invention provides a method for evaluating the conversion coefficient of hydroxyl radicals ∙OH produced by oxidation of soil minerals, wherein step S102 comprises:

[0014] Receive the evaluation target, and determine the comparison indicators according to the evaluation target, the comparison indicators including the amount of hydroxyl free radicals generated, the reaction rate, and the time dependence;

[0015] Compare the comparison index in the experimental simulation result with the corresponding historical experimental data in the database numerically to obtain the comparison result, calculate the error between the experimental simulation result and the historical experimental data, and obtain the error analysis result;

[0016] Based on the comparison results and error analysis results, it is determined whether the experimental simulation results are consistent with the historical experimental data in the database. If the error is within the preset range, the laboratory simulation model is considered to be qualified.

[0017] Furthermore, the present invention provides a method for evaluating the conversion coefficient of hydroxyl radicals ∙OH produced by oxidation of soil minerals, wherein step S103 comprises:

[0018] A chemical probe is used to capture and quantify hydroxyl radicals, and the amount of hydroxyl radicals generated is calculated by measuring the concentration of products after the chemical probe reacts with the hydroxyl radicals. The chemical probe includes benzoate.

[0019] Furthermore, the present invention provides a method for evaluating the conversion coefficient of hydroxyl radicals ∙OH produced by oxidation of soil minerals, wherein step S103 comprises:

[0020] Obtaining soil sample data to be used, including sample source, sample type, sample composition, laboratory simulation requirements and experimental conditions;

[0021] According to the laboratory simulation requirements, the pre-processed sample data is substituted into the laboratory simulation model, the simulation conditions of temperature, humidity, pH value, added chemical reagents are set, and the simulation process is started;

[0022] The laboratory simulation model runs the simulation algorithm according to the input sample data and the set simulation conditions to generate the simulation experiment results of the expected sample. The simulation experiment results of the expected sample include the generation amount of hydroxyl radicals, reaction rate, and time dependence.

[0023] The simulation experiment results are post-processed to extract indicators corresponding to the sample data expected to be used, the extracted indicators are matched with the sample data expected to be used, and the matched simulation experiment results and the sample data expected to be used are integrated into a simulation data set, which includes simulation results and sample information.

[0024] Furthermore, the present invention provides a method for evaluating the conversion coefficient of hydroxyl radicals ∙OH produced by oxidation of soil minerals, wherein step S104 comprises:

[0025] Collect data on actual soil environmental field parameters such as temperature, humidity, pH value, and hydroxyl radical production from soil mineral oxidation;

[0026] And establish collection time labels and location labels for the collected data;

[0027] Preprocess the collected field monitoring data in the actual soil environment, substitute the preprocessed data into the kinetic model of hydroxyl radical production by soil mineral oxidation, set the model parameters and run the model;

[0028] The kinetic model will run the simulation algorithm based on the input field monitoring data in the actual soil environment and output the simulation results of soil mineral oxidation to produce hydroxyl radicals.

[0029] Furthermore, the present invention provides a method for evaluating the conversion coefficient of hydroxyl radicals ∙OH produced by oxidation of soil minerals, wherein step S104 comprises:

[0030] Extract the production amount and time dependence of hydroxyl radical indicators in the simulation results, compare the simulation results with the field monitoring data in the actual soil environment item by item, and analyze the consistency and differences in key indicators;

[0031] Calculate the error of the comparison result. If the error of the comparison result is within the preset range, the dynamic model is considered qualified in the actual soil environment.

[0032] If the error of the comparison result exceeds the preset range, the dynamic model needs to be optimized;

[0033] If the kinetic model is qualified in the actual soil environment, the field monitoring data in the actual soil environment are evaluated.

[0034] Furthermore, the present invention provides a method for evaluating the conversion coefficient of hydroxyl radicals ∙OH produced by oxidation of soil minerals, wherein step S105 comprises:

[0035] According to the evaluation target and the characteristics of the conversion coefficient of hydroxyl radical produced by soil mineral oxidation, a suitable evaluation model is constructed, and the pre-processed field monitoring data are input into the evaluation model. The pre-processed field monitoring data include the environmental parameters temperature, humidity, pH value and the field monitoring data of hydroxyl radical produced by soil mineral oxidation;

[0036] The evaluation model is started to process the input field monitoring data. The evaluation model calculates the evaluation result of the conversion coefficient of soil mineral oxidation to produce hydroxyl radicals based on the built-in algorithm and logic.

[0037] The beneficial effects of the present invention are mainly reflected in the following aspects:

[0038] By establishing a laboratory simulation model and a kinetic model, the present invention can more accurately simulate the oxidation process of soil minerals and the amount of hydroxyl radicals produced under different environmental conditions (such as temperature, humidity, pH value, etc.), thereby improving the accuracy of the assessment of the conversion coefficient of soil mineral oxidation to hydroxyl radicals.

[0039] Compared with field experiments, laboratory simulation experiments can greatly save time, manpower and material costs. Through model simulation, the complexity and uncontrollable factors brought by field experiments can be avoided, thereby reducing the overall experimental cost. By comparing and verifying the laboratory simulation results with historical experimental data, the accuracy and reliability of the model are verified. At the same time, by collecting field monitoring data and verifying the model in actual soil environments, the applicability and reliability of the model in practical applications are further enhanced.

[0040] The evaluation method provided by the present invention can not only obtain the specific value of the conversion coefficient of hydroxyl radicals produced by soil mineral oxidation, but also reveal the internal mechanism and key influencing factors of the soil mineral oxidation process, providing strong support for the in-depth study of soil environmental science and agricultural technology. The evaluation results can provide a scientific basis for agricultural production and soil management, and guide the formulation of decisions such as soil improvement, fertilizer application, and pollution control. By optimizing soil environmental management measures, crop yield and quality can be improved, and agricultural sustainable development can be promoted.

[0041] Based on the evaluation method and kinetic model provided by the present invention, the development and application of new technologies can be further guided. For example, by simulating the soil mineral oxidation process under different management measures, the potential and effect of new technologies in improving the soil environment can be evaluated, promoting the innovative development of soil environmental science and agricultural technology.

[0042] In summary, the present invention not only improves the accuracy and reliability of the evaluation results through a scientific and systematic evaluation method, but also reduces the experimental cost, enhances the applicability and reliability of the model, provides strong support for the optimization of agricultural production and soil management, and promotes scientific research and technological innovation in related fields. BRIEF DESCRIPTION OF THE DRAWINGS

[0043] In order to more clearly illustrate the technical solution of the present invention, the following briefly introduces the drawings required for use in the embodiments. Obviously, for ordinary technicians in this field, other drawings can be obtained based on these drawings without creative work.

[0044] Figure 1 A schematic diagram of a method for evaluating the conversion coefficient of hydroxyl radicals ∙OH produced by oxidation of soil minerals provided in an embodiment of the present invention. DETAILED DESCRIPTION

[0045] In order to make the purpose, technical solution and advantages of the present invention clearer, the technical solution of the present invention will be clearly and completely described in conjunction with the specific embodiments of the present invention and the corresponding drawings. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention. The technical solutions provided by the embodiments of the present invention are described in detail below in conjunction with the drawings.

[0046] In order to better understand the purpose of the present invention, the present invention is described in further detail below.

[0047] The present invention provides a method for evaluating the conversion coefficient of hydroxyl radicals ∙OH produced by oxidation of soil minerals, comprising:

[0048] Step S101, acquiring sample data and experimental history data, preprocessing the sample data and the experimental history data to obtain preprocessed sample data and preprocessed experimental history data;

[0049] Get sample data and experimental history data:

[0050] Sample data includes specific information of soil samples, such as sample source, type, composition, etc., which is crucial for understanding the characteristics and chemical properties of soil minerals.

[0051] Experimental historical data refers to experimental data conducted in the past related to the production of hydroxyl radicals by soil mineral oxidation, including records of soil mineral oxidation processes under different conditions, measurements of hydroxyl radical production, etc. Historical data provide valuable references for building and verifying models.

[0052] Preprocess sample data and experimental history data: remove or correct errors, outliers, or missing values ​​in the data. For example, check for unreasonable ranges or duplicate records in the data, as well as the accuracy and consistency of the data. Convert the data into a format suitable for subsequent analysis and modeling. This includes converting text data into numerical data, unifying measurement units, etc.

[0053] The data is scaled as needed to eliminate the impact of different dimensions between features so that all features have the same importance in the model. The features that have an important impact on the evaluation model are screened out from the original data. These features are closely related to the types of soil minerals and environmental conditions (such as temperature, humidity, pH value, etc.).

[0054] After the above preprocessing steps, the sample data and experimental history data will be organized into data sets with clear structure, unified format and reliable quality. These data sets will be used to build laboratory simulation models and kinetic models in the future.

[0055] Preprocessing can correct errors and outliers in the data, reduce noise and bias, and thus improve the accuracy and robustness of the model. Through data standardization and normalization, the model training speed can be accelerated and the computational efficiency can be improved. Feature selection helps the model focus on features that have an important impact on the evaluation results and avoid overfitting and underfitting problems.

[0056] Step S101 provides high-quality data support for the subsequent construction of laboratory simulation models and kinetic models. Through meticulous data preprocessing, the accuracy and reliability of the model lay a solid foundation for subsequent analysis and evaluation.

[0057] Step S102, using the preprocessed experimental historical data to build a laboratory simulation model, retrieve sample data from the database, substitute the sample data from the database into the preset laboratory simulation model, obtain experimental simulation results, compare the experimental simulation results with the historical experimental data in the database, if the data comparison results are consistent, the laboratory simulation model is qualified;

[0058] After preprocessing, the experimental historical data includes high-quality information about the oxidation process of soil minerals under different conditions. This information is the basis for building a laboratory simulation model. Based on these experimental historical data, appropriate mathematical modeling methods (such as regression analysis, machine learning algorithms, etc.) are used to build a laboratory simulation model that can simulate the oxidation process of soil minerals.

[0059] The sample data in the database contains information about a variety of soil samples, which will be used to test the accuracy and reliability of the laboratory simulation model. Substitute the sample data in the database into the preset laboratory simulation model: Input the sample data retrieved from the database into the already constructed laboratory simulation model to execute the simulation process.

[0060] The laboratory simulation model generates corresponding simulation experimental results based on the input sample data. These results include the simulation of the oxidation process of soil minerals under different environmental conditions (such as temperature, humidity, pH value, etc.), and the predicted value of the amount of hydroxyl radicals produced.

[0061] The simulation results are compared with the corresponding historical experimental data in the database, including key indicators such as hydroxyl radical generation, reaction rate, and time dependence. If the comparison results show that the simulation results are consistent with the historical experimental data or the error is within the preset range, it is considered that the laboratory simulation model can accurately simulate the oxidation process of soil minerals and the model test is qualified.

[0062] If there are significant differences in the comparison results or the error exceeds the preset range, the laboratory simulation model needs to be adjusted and optimized until a satisfactory simulation effect is achieved. By constructing a laboratory simulation model that can accurately simulate the soil mineral oxidation process, a reliable basis can be provided for the subsequent kinetic model construction and evaluation.

[0063] Compared with field experiments, laboratory simulation can greatly save time, manpower and material costs, while avoiding the uncontrollable factors brought by field experiments. Laboratory simulation models can flexibly adjust environmental parameters and simulate the soil mineral oxidation process under different conditions, providing support for a deeper understanding of the oxidation reaction mechanism.

[0064] Step S102 is a key step in building and verifying the laboratory simulation model, which ensures the accuracy and reliability of subsequent analysis and evaluation. Through a rigorous comparative verification process, the laboratory simulation model can accurately simulate the oxidation process of soil minerals, laying a solid foundation for the subsequent kinetic model construction and conversion coefficient evaluation.

[0065] Step S103, receiving the sample data expected to be used, substituting the sample data expected to be used into the laboratory simulation model, generating simulation experiment results of the samples expected to be used, matching the simulation experiment results of the samples expected to be used with the sample data expected to be used, obtaining a simulation data set, and constructing a kinetic model of hydroxyl radical production by oxidation of soil minerals using the experimental history data and the simulation data set. The kinetic model of hydroxyl radical production by oxidation of soil minerals is used to simulate the oxidation process of soil minerals under different environmental conditions under laboratory conditions. The control parameters under different environmental conditions include controlling temperature, humidity, pH value, and added chemical reagents. The amount of hydroxyl radicals produced and the change pattern of hydroxyl radicals over time are recorded according to the oxidation process of soil minerals under different environmental conditions.

[0066] Receive the expected sample data, including data on the soil samples expected to be used in the assessment process, such as sample source, type, composition, etc. These data will be used in subsequent simulation experiments.

[0067] The received data of the expected usage samples are input into the validated laboratory simulation model, which will simulate the oxidation process of soil minerals under different conditions based on these data.

[0068] The laboratory simulation model runs the simulation algorithm based on the input sample data to generate simulation experimental results. These results reflect the oxidation process of soil minerals and the amount of hydroxyl radicals produced under different environmental conditions (such as temperature, humidity, pH value, etc.). The simulation experimental results are matched with the sample data expected to be used, and the correspondence between the simulation results and the sample data is accurate. This step helps with subsequent data analysis and processing.

[0069] The matched simulation experiment results and the expected sample data are integrated into a simulation data set. This set contains various key indicators and sample information of the simulation experiment, providing necessary data support for the subsequent construction of the kinetic model.

[0070] Based on the experimental historical data and simulation data sets, appropriate mathematical modeling methods (such as differential equation models, machine learning methods, etc.) are used to construct a kinetic model for the production of hydroxyl radicals by soil mineral oxidation. This model can describe the oxidation process of soil minerals under different environmental conditions and its main influencing factors.

[0071] The kinetic model was used to simulate the oxidation process of soil minerals under different environmental conditions under laboratory conditions. These environmental conditions include temperature, humidity, pH value, and added chemical reagents. By adjusting these parameters, the model can predict the amount of hydroxyl radicals produced under different conditions and how it changes over time.

[0072] Providing in-depth insights: Kinetic models can reveal the intrinsic mechanism and key influencing factors of soil mineral oxidation processes, providing a scientific basis for soil environmental management and agricultural production. By simulating the oxidation process under different conditions, kinetic models can help decision makers evaluate the effects of different management measures and formulate scientific and reasonable soil management plans.

[0073] The prediction results based on the kinetic model can guide the research and development and application of new technologies and promote the innovative development of soil environmental science and agricultural technology.

[0074] Step S104, collecting on-site monitoring data in the actual soil environment, the on-site monitoring data in the actual soil environment includes environmental parameters and data on hydroxyl radical production by soil mineral oxidation, substituting the on-site monitoring data in the actual soil environment into a kinetic model of hydroxyl radical production by soil mineral oxidation, outputting a simulation result of hydroxyl radical production by soil mineral oxidation, and comparing the simulation result of hydroxyl radical production by soil mineral oxidation with the on-site monitoring data in the actual soil environment. If the error of the comparison result is within a preset range, evaluating the on-site monitoring data in the actual soil environment;

[0075] Collect on-site monitoring data in the actual soil environment. In the actual soil environment, collect environmental parameters (such as temperature, humidity, pH value, etc.) and direct or indirect data on the production of hydroxyl radicals by soil mineral oxidation. These data are a true reflection of the evaluation of the soil mineral oxidation process and its conversion coefficient.

[0076] The collected data is labeled with the time and location of collection, as well as the traceability and accuracy of the data. These labels facilitate subsequent data analysis and comparison.

[0077] The collected field monitoring data is preprocessed as necessary, including data cleaning, outlier processing, missing value filling, etc., as well as data quality and consistency.

[0078] Substitute the pre-processed actual soil environment field monitoring data into the previously constructed kinetic model of soil mineral oxidation to produce hydroxyl radicals. This step is the key to verifying the applicability and accuracy of the model in the actual environment.

[0079] Set the model parameters and run the kinetic model to simulate the oxidation process of soil minerals based on the input actual environmental data, and output the simulation results of soil mineral oxidation to produce hydroxyl radicals.

[0080] The simulation results of the kinetic model were compared item by item with the field monitoring data in the actual soil environment, focusing on the consistency and differences of key indicators such as the amount of hydroxyl radicals produced and time dependence.

[0081] Calculate the error of the comparison results and determine whether the error is within the preset range. Error analysis is an important means to evaluate the accuracy and reliability of dynamic model prediction.

[0082] If the error of the comparison result is within the preset range, it is considered that the kinetic model performs well in the actual soil environment and can effectively evaluate the field monitoring data in the actual soil environment. If the error of the comparison result exceeds the preset range, the kinetic model needs to be optimized and adjusted to improve the prediction accuracy and reliability of the model.

[0083] Step S104 verifies the effectiveness and accuracy of the kinetic model in practical applications by collecting on-site monitoring data in an actual soil environment and comparing and analyzing the data with the simulation results of the kinetic model. This process not only provides a reliable basis for the evaluation of the conversion coefficient of hydroxyl radicals produced by soil mineral oxidation, but also provides important feedback for the subsequent optimization and application of the kinetic model. Through continuous model verification and optimization, the scientificity and practicality of the evaluation method can be further improved.

[0084] Step S105, preprocessing the field monitoring data in the actual soil environment, substituting the preprocessed field monitoring data in the actual soil environment into a preset evaluation model to obtain an evaluation result of the conversion coefficient of soil mineral oxidation to produce hydroxyl radicals.

[0085] Step S105 is the final step in the entire evaluation process. It focuses on calculating the specific evaluation results of the conversion coefficient of hydroxyl radicals produced by soil mineral oxidation using a preset evaluation model after a series of preprocessing steps of the field monitoring data in the actual soil environment. The following is a detailed explanation of this step:

[0086] Preprocess the field monitoring data collected in the actual soil environment. Preprocessing includes data cleaning (removing noise, outliers, etc.), format conversion, standardization (such as normalization, standardization, etc.) and feature selection. The purpose of preprocessing is to improve the quality and consistency of the input data to meet the evaluation model's requirements for input data format and quality.

[0087] According to the evaluation objectives and the characteristics of the conversion coefficient of hydroxyl radicals produced by soil mineral oxidation, a suitable evaluation model is selected or constructed. This model is based on machine learning, statistical models or other mathematical methods, depending on the complexity of the problem and the characteristics of the available data.

[0088] Substitute the pre-processed field monitoring data in the actual soil environment (including environmental parameters such as temperature, humidity, pH value, and field monitoring data of hydroxyl radicals produced by soil mineral oxidation) into the preset evaluation model. This step is the key link to combine the actual data with the evaluation logic.

[0089] Start the assessment model and let it process the input field monitoring data. The assessment model will analyze and calculate the data based on the built-in algorithms and logic.

[0090] After the evaluation model completes data processing, it will output the evaluation result of the conversion coefficient of hydroxyl radicals produced by soil mineral oxidation. This result is a specific value, an interval or a probability distribution, depending on the output form of the evaluation model.

[0091] Interpret the assessment results, understand their meaning, and apply them to actual soil management and agricultural production. The assessment results can guide the making of decisions such as soil improvement, fertilizer application, and pollution control.

[0092] Specifically, the present invention provides a method for evaluating the conversion coefficient of hydroxyl radicals ∙OH produced by oxidation of soil minerals, wherein step S102 comprises:

[0093] Receive the evaluation target, and determine the comparison indicators according to the evaluation target, the comparison indicators including the amount of hydroxyl free radicals generated, the reaction rate, and the time dependence;

[0094] Compare the comparison index in the experimental simulation result with the corresponding historical experimental data in the database numerically to obtain the comparison result, calculate the error between the experimental simulation result and the historical experimental data, and obtain the error analysis result;

[0095] Based on the comparison results and error analysis results, it is determined whether the experimental simulation results are consistent with the historical experimental data in the database. If the error is within the preset range, the laboratory simulation model is considered to be qualified.

[0096] Specifically, the present invention provides a method for evaluating the conversion coefficient of hydroxyl radicals ∙OH produced by oxidation of soil minerals, wherein step S103 comprises:

[0097] A chemical probe is used to capture and quantify hydroxyl radicals, and the amount of hydroxyl radicals generated is calculated by measuring the concentration of products after the chemical probe reacts with the hydroxyl radicals. The chemical probe includes benzoate.

[0098] Specifically, the present invention provides a method for evaluating the conversion coefficient of hydroxyl radicals ∙OH produced by oxidation of soil minerals, wherein step S103 comprises:

[0099] Obtaining soil sample data to be used, including sample source, sample type, sample composition, laboratory simulation requirements and experimental conditions;

[0100] According to the laboratory simulation requirements, the pre-processed sample data is substituted into the laboratory simulation model, the simulation conditions of temperature, humidity, pH value, added chemical reagents are set, and the simulation process is started;

[0101] The laboratory simulation model runs the simulation algorithm according to the input sample data and the set simulation conditions to generate the simulation experiment results of the expected sample. The simulation experiment results of the expected sample include the generation amount of hydroxyl radicals, reaction rate, and time dependence.

[0102] The simulation experiment results are post-processed to extract indicators corresponding to the sample data expected to be used, the extracted indicators are matched with the sample data expected to be used, and the matched simulation experiment results and the sample data expected to be used are integrated into a simulation data set, which includes simulation results and sample information.

[0103] Specifically, the present invention provides a method for evaluating the conversion coefficient of hydroxyl radicals ∙OH produced by oxidation of soil minerals, wherein step S104 comprises:

[0104] Collect data on actual soil environmental field parameters such as temperature, humidity, pH value, and hydroxyl radical production from soil mineral oxidation;

[0105] And establish collection time labels and location labels for the collected data;

[0106] Preprocess the collected field monitoring data in the actual soil environment, substitute the preprocessed data into the kinetic model of hydroxyl radical production by soil mineral oxidation, set the model parameters and run the model;

[0107] The kinetic model will run the simulation algorithm based on the input field monitoring data in the actual soil environment and output the simulation results of soil mineral oxidation to produce hydroxyl radicals.

[0108] Specifically, the present invention describes in detail in step S104 how to collect field monitoring data in an actual soil environment and apply it to the kinetic model of soil mineral oxidation to produce hydroxyl radicals to verify the validity of the model and output simulation results. The following is a detailed breakdown of this step:

[0109] First, a series of key environmental parameters need to be collected in the actual soil environment, including temperature, humidity, pH value, etc. These parameters are crucial to understanding the overall state of the soil environment.

[0110] At the same time, it is also necessary to collect direct or indirect data on the production of hydroxyl radicals by oxidation of soil minerals. These data are obtained through specific sensors or chemical analysis methods to reflect the oxidation process of soil minerals in actual environments.

[0111] The collected data are labeled with time and location. The time label records the specific time point of data collection, which helps to consider the impact of time factors on the soil environment in subsequent data analysis.

[0112] Location tags mark the specific location where the data was collected, making it easier to identify and analyze differences in soil environments between different geographical locations.

[0113] The collected field monitoring data is subjected to necessary preprocessing steps, as well as the quality and consistency of the data. Preprocessing includes data cleaning (removing outliers, filling missing values, etc.), data format conversion, standardization or normalization, etc.

[0114] The preprocessing step helps to improve the accuracy of the input data to the kinetic model, thereby improving the predictive performance of the model.

[0115] Substitute the actual soil environmental field monitoring data after pretreatment into the previously constructed kinetic model of soil mineral oxidation to produce hydroxyl radicals. This step is the key link between the actual observation data and the theoretical model.

[0116] Set the corresponding parameters in the kinetic model, such as controlling temperature, humidity, pH value, etc., to simulate the conditions in the actual soil environment. Run the kinetic model, and the model will calculate and output the simulation results of soil mineral oxidation to produce hydroxyl radicals based on the input actual soil environment data using the built-in simulation algorithm.

[0117] The simulation results output by the kinetic model will provide detailed information on the process of hydroxyl radical production by soil mineral oxidation, including the amount of hydroxyl radicals produced and their time dependence.

[0118] Step S104 successfully combines the actual observation data with the theoretical model through data collection, label establishment, data preprocessing and kinetic model operation in the actual soil environment, providing a scientific basis for the simulation of the process of hydroxyl radical production by soil mineral oxidation. This process not only verifies the application effect of the kinetic model in the actual environment, but also provides strong data support for subsequent soil management and agricultural production.

[0119] Specifically, the present invention provides a method for evaluating the conversion coefficient of hydroxyl radicals ∙OH produced by oxidation of soil minerals, wherein step S104 comprises:

[0120] Extract the production amount and time dependence of hydroxyl radical indicators in the simulation results, compare the simulation results with the field monitoring data in the actual soil environment item by item, and analyze the consistency and differences in key indicators;

[0121] Calculate the error of the comparison result. If the error of the comparison result is within the preset range, the dynamic model is considered qualified in the actual soil environment.

[0122] If the error of the comparison result exceeds the preset range, the dynamic model needs to be optimized;

[0123] If the kinetic model is qualified in the actual soil environment, the field monitoring data in the actual soil environment are evaluated.

[0124] Verify the kinetic model and evaluate the field monitoring data. Extract key indicators from the simulation results of the kinetic model, mainly the amount of hydroxyl radicals produced and their time dependence. These indicators directly reflect the generation characteristics and dynamic changes of hydroxyl radicals during the oxidation of soil minerals. Compare the key indicators such as the amount of hydroxyl radicals produced and time dependence in the simulation results with the corresponding data monitored on site in the actual soil environment. This step aims to verify whether the simulation results can truly reflect the generation of hydroxyl radicals in the actual soil environment.

[0125] During the comparison process, it is necessary to carefully analyze the consistency and difference between the simulation results and the actual data in key indicators. The consistency indicates that the kinetic model can simulate the actual soil environment well, while the difference indicates that there are areas in the model that need improvement.

[0126] After comparing the results, it is necessary to calculate the error between the simulation results and the actual data. The error calculation method can include absolute error, relative error and other forms, depending on the evaluation requirements and the characteristics of the data.

[0127] The calculated error is used to determine the suitability of the dynamic model in the actual soil environment. If the error is within the preset range, the model is considered to be able to adapt to the actual environment well and is considered qualified; otherwise, the model needs to be optimized.

[0128] If the model is unqualified (i.e. the error exceeds the preset range), the dynamic model needs to be optimized based on the comparison results and error analysis. Optimization involves adjusting model parameters, improving simulation algorithms, adding considerations, and other aspects.

[0129] On the premise of confirming that the dynamic model is qualified, the field monitoring data in the actual soil environment can be evaluated. The evaluation includes the validity and reliability of the analysis data and the soil environment status they reflect.

[0130] Specifically, the present invention provides a method for evaluating the conversion coefficient of hydroxyl radicals ∙OH produced by oxidation of soil minerals, wherein step S105 comprises:

[0131] According to the evaluation target and the characteristics of the conversion coefficient of hydroxyl radical produced by soil mineral oxidation, a suitable evaluation model is constructed, and the pre-processed field monitoring data are input into the evaluation model. The pre-processed field monitoring data include the environmental parameters temperature, humidity, pH value and the field monitoring data of hydroxyl radical produced by soil mineral oxidation;

[0132] The evaluation model is started to process the input field monitoring data. The evaluation model calculates the evaluation result of the conversion coefficient of soil mineral oxidation to produce hydroxyl radicals based on the built-in algorithm and logic.

[0133] According to the evaluation objectives and the characteristics of the conversion coefficient of soil mineral oxidation to hydroxyl radicals, a suitable evaluation model is first constructed. This model needs to be able to process and analyze the complex data in the process of soil mineral oxidation to hydroxyl radicals, including environmental parameters (such as temperature, humidity, pH value) and field monitoring data of soil mineral oxidation to hydroxyl radicals.

[0134] The field monitoring data collected in the actual soil environment are preprocessed. The preprocessing steps include data cleaning (removing outliers, filling missing values, etc.), data standardization or normalization, and organizing the environmental parameters (temperature, humidity, pH value) and soil mineral oxidation hydroxyl radical production data into a format that can be recognized by the model.

[0135] The pre-processed field monitoring data (including environmental parameters and soil mineral oxidation hydroxyl radical production data) are input into the evaluation model. The accuracy and consistency of the data are crucial to the accuracy of the model output.

[0136] Start the built assessment model and start processing the input field monitoring data. The model will analyze and calculate the data based on the built-in algorithms and logic.

[0137] The evaluation model uses a built-in algorithm and input data to calculate the specific evaluation results of the conversion coefficient of soil mineral oxidation to produce hydroxyl radicals. This result reflects the conversion efficiency or ability of soil mineral oxidation to produce hydroxyl radicals under specific environmental conditions.

[0138] The evaluation results are output and analyzed, which can help understand the oxidation behavior of soil minerals under different environmental conditions, as well as the generation law and conversion efficiency of hydroxyl radicals. This has important guiding significance for soil management and agricultural production.

[0139] Step S105 is the last step of the evaluation method. By constructing a suitable evaluation model and processing the field monitoring data in the actual soil environment, the specific evaluation results of the conversion coefficient of hydroxyl radicals produced by soil mineral oxidation are calculated. This process not only provides a scientific basis for the in-depth study of soil mineral oxidation behavior, but also provides strong support for the practical application of soil management and agricultural production. The accuracy and reliability of the evaluation results are of great significance for guiding agricultural production practices and optimizing soil management measures.

[0140] The technical solution of the present invention effectively solves the problem that it is difficult to fully simulate the complexity and dynamics of the actual soil environment under laboratory conditions, and thus accurately evaluate the conversion coefficient of hydroxyl radicals produced by soil mineral oxidation. The specific solution includes the following aspects:

[0141] First, by acquiring and preprocessing sample data and experimental historical data, a laboratory simulation model is constructed using these data. Then, the sample data in the database is substituted into the model for simulation, and the simulation results are compared with the historical experimental data in the database to verify the accuracy and reliability of the model. This step ensures that the laboratory simulation model can more realistically reflect the oxidation process of soil minerals.

[0142] By receiving the expected sample data and substituting it into the laboratory simulation model, the simulation experiment results are generated. Subsequently, the simulation experiment results are matched with the sample data, and combined with the experimental historical data, a kinetic model of soil mineral oxidation to produce hydroxyl radicals is constructed. The model can simulate the oxidation process of soil minerals under different environmental conditions (such as temperature, humidity, pH value, added chemical reagents, etc.), and record the amount of hydroxyl radicals produced and its change over time.

[0143] Conduct field monitoring in the actual soil environment and collect data on environmental parameters and the production of hydroxyl radicals by soil mineral oxidation. Substitute these data into the previously constructed kinetic model, output the simulation results, and compare them with the actual monitoring data. Through error analysis of the comparison results, verify the applicability and accuracy of the kinetic model in the actual soil environment. If the error is large, optimize the model.

[0144] After preprocessing the field monitoring data in the actual soil environment, it was substituted into the preset evaluation model to calculate the evaluation results of the conversion coefficient of hydroxyl radicals produced by soil mineral oxidation. This step is based on the verified kinetic model and field monitoring data, ensuring the accuracy and reliability of the evaluation results.

[0145] Through the above steps, the present invention can not only simulate the oxidation process of soil minerals under laboratory conditions, but also verify the effectiveness of the simulation results and kinetic models through field monitoring data of the actual soil environment, thereby solving the problem that it is difficult for the laboratory to fully simulate the complexity and dynamics of the actual soil environment, and realizing an accurate evaluation of the conversion coefficient of hydroxyl radicals produced by the oxidation of soil minerals. This method improves the scientificity and practicality of the evaluation and provides strong support for research in the fields of agriculture and the environment.

[0146] Obviously, those skilled in the art can make various changes and modifications to the present invention without departing from the spirit and scope of the present invention. Thus, if these modifications and variations of the present invention fall within the scope of the claims of the present invention and their equivalents, the present invention is also intended to include these modifications and variations, and the above-described embodiments of the present invention do not constitute a limitation on the scope of protection of the present invention.

Claims

1. A method for evaluating the conversion coefficient of hydroxyl radicals ∙OH produced by oxidation of soil minerals, characterized in that: include: Step S101, acquiring sample data and experimental history data, preprocessing the sample data and the experimental history data to obtain preprocessed sample data and preprocessed experimental history data; Step S102, using the preprocessed experimental historical data to build a laboratory simulation model, retrieve sample data from the database, substitute the sample data from the database into the preset laboratory simulation model, obtain experimental simulation results, compare the experimental simulation results with the historical experimental data in the database, if the data comparison results are consistent, the laboratory simulation model is qualified; Step S103, receiving the sample data expected to be used, substituting the sample data expected to be used into the laboratory simulation model, generating simulation experiment results of the samples expected to be used, matching the simulation experiment results of the samples expected to be used with the sample data expected to be used, obtaining a simulation data set, and constructing a kinetic model of hydroxyl radical production by oxidation of soil minerals using the experimental history data and the simulation data set. The kinetic model of hydroxyl radical production by oxidation of soil minerals is used to simulate the oxidation process of soil minerals under different environmental conditions under laboratory conditions. The control parameters under different environmental conditions include controlling temperature, humidity, pH value, and added chemical reagents. The amount of hydroxyl radicals produced and the change pattern of hydroxyl radicals over time are recorded according to the oxidation process of soil minerals under different environmental conditions. Step S104, collecting on-site monitoring data in the actual soil environment, the on-site monitoring data in the actual soil environment includes environmental parameters and data on hydroxyl radical production by soil mineral oxidation, substituting the on-site monitoring data in the actual soil environment into a kinetic model of hydroxyl radical production by soil mineral oxidation, outputting a simulation result of hydroxyl radical production by soil mineral oxidation, and comparing the simulation result of hydroxyl radical production by soil mineral oxidation with the on-site monitoring data in the actual soil environment. If the error of the comparison result is within a preset range, evaluating the on-site monitoring data in the actual soil environment; Step S105, preprocessing the field monitoring data in the actual soil environment, substituting the preprocessed field monitoring data in the actual soil environment into a preset evaluation model to obtain an evaluation result of the conversion coefficient of hydroxyl radicals produced by soil mineral oxidation; The step S102 includes: Receive the evaluation target, and determine the comparison indicators according to the evaluation target, the comparison indicators including the amount of hydroxyl free radicals generated, the reaction rate, and the time dependence; Compare the comparison index in the experimental simulation result with the corresponding historical experimental data in the database numerically to obtain the comparison result, calculate the error between the experimental simulation result and the historical experimental data, and obtain the error analysis result; According to the comparison results and error analysis results, it is judged whether the experimental simulation results are consistent with the historical experimental data in the database. If the error is within the preset range, the laboratory simulation model is considered qualified; The step S103 includes: Obtaining soil sample data to be used, including sample source, sample type, sample composition, laboratory simulation requirements and experimental conditions; According to the laboratory simulation requirements, the pre-processed sample data is substituted into the laboratory simulation model, the simulation conditions of temperature, humidity, pH value, added chemical reagents are set, and the simulation process is started; The laboratory simulation model runs the simulation algorithm according to the input sample data and the set simulation conditions to generate the simulation experiment results of the expected sample. The simulation experiment results of the expected sample include the generation amount of hydroxyl radicals, reaction rate, and time dependence. The simulation experiment results are post-processed to extract indicators corresponding to the sample data expected to be used, the extracted indicators are matched with the sample data expected to be used, and the matched simulation experiment results and the sample data expected to be used are integrated into a simulation data set, which includes simulation results and sample information.

2. The method for evaluating the conversion coefficient of hydroxyl radicals produced by oxidation of soil minerals as claimed in claim 1, characterized in that: The step S103 includes: A chemical probe is used to capture and quantify hydroxyl radicals, and the amount of hydroxyl radicals generated is calculated by measuring the concentration of products after the chemical probe reacts with the hydroxyl radicals. The chemical probe includes benzoate.

3. The method for evaluating the conversion coefficient of hydroxyl radicals produced by oxidation of soil minerals according to claim 1, characterized in that: The step S104 includes: Collect data on actual soil environmental field parameters such as temperature, humidity, pH value, and hydroxyl radical production from soil mineral oxidation; And establish collection time labels and location labels for the collected data; Preprocess the collected field monitoring data in the actual soil environment, substitute the preprocessed data into the kinetic model of hydroxyl radical production by soil mineral oxidation, set the model parameters and run the model; The kinetic model will run the simulation algorithm based on the input field monitoring data in the actual soil environment and output the simulation results of soil mineral oxidation to produce hydroxyl radicals.

4. The method for evaluating the conversion coefficient of hydroxyl radicals produced by oxidation of soil minerals as claimed in claim 3, characterized in that: The step S104 includes: Extract the production amount and time dependence of hydroxyl radical indicators in the simulation results, compare the simulation results with the field monitoring data in the actual soil environment item by item, and analyze the consistency and differences in key indicators; Calculate the error of the comparison result. If the error of the comparison result is within the preset range, the dynamic model is considered qualified in the actual soil environment. If the error of the comparison result exceeds the preset range, the dynamic model needs to be optimized; If the kinetic model is qualified in the actual soil environment, the field monitoring data in the actual soil environment are evaluated.

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